Abstract
Abstract The computer distance teaching system teaches through the network, and there is no entrance threshold. Any student who is willing to study can log in to the network computer distance teaching system for study at any free time. Neural network has a strong self-learning ability and is an important part of artificial intelligence research. Based on this study, a neural network-embedded architecture based on shared memory and bus structure is proposed. By looking for an alternative method of exp function to improve the speed of radial basis function algorithm, and then by analyzing the judgment conditions in the main loop during the algorithm process, these judgment conditions are modified conditionally to reduce the calculation scale, which can double the speed of the algorithm. Finally, this article verifies the function, performance, and interface of the computer distance education system.
Highlights
Distance education is an educational behavior spanning geographical space
This study puts forward a solution of computer distance teaching system based on neural network algorithm and embedded system
Optimization algorithm is the core of intelligent algorithm, so the embedded application of intelligent algorithm has similar problems, that is, the embedded application of optimization algorithm
Summary
Distance education is an educational behavior spanning geographical space. Teachers and students may not be in the same classroom, or even in the same region or country. Considering the above problems, this article is dedicated to designing and implementing a distance education platform based on neural network algorithm and embedded system technology. The number of hidden nodes in the network is equal to the number of input samples, and the activation function of hidden nodes is often Gaussian RBF. When a certain sample Xp in the training set is input, the corresponding expected output dp is the teacher signal. To determine the P weights between the hidden layer and the output layer of the network, it is necessary to input the samples in the training set one by one. For the input mode of nontraining set, the output value of the network is equivalent to the interpolation of functions, so the RBF network can be used as number approximation. The regularized RBF network has the following three characteristics: (1) Regularized network is a general approximator, which can approximate any multivariate continuous function on a compact set with arbitrary precision only with enough nodes. (2) It has the best approximation property; that is, if an unknown nonlinear function f is given, a set of weights can always be found, which makes the approximation of regularization network to f better than all its possible choices. (3) The solution obtained by regularizing the network is the best, and the so-called “best” is reflected in satisfying both the approximation error of the sample and the smoothness of the approximation curve
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